Semantic clustering based retrieval for candidate set expansion

ABSTRACT

In an example embodiment, a machine learning algorithm is used to train a query-based deep semantic similarity neural network to output a query context vector in a vector space that includes both query context vectors and document context vectors. Both the query context vectors and document context vectors are clustered using a clustering algorithm. When an input search query is obtained, the input search query is also passed into the query-based deep semantic similarity neural network and its output document context vector assigned to a first cluster based on the clustering algorithm. Documents within the first cluster are then retrieved in response to the input search query.

TECHNICAL FIELD

The present disclosure generally relates to technical problemsencountered in performing job searches on computer networks. Morespecifically, the present disclosure relates to the use of semanticsimilarity in a machine-learned job posting result ranking model tosolve technical problems such as cross-language retrieval and rankingand retrieval degradation due to query preprocessing errors.

BACKGROUND

The rise of the Internet has occasioned two disparate phenomena: theincrease in the presence of social networks, with their correspondingmember profiles visible to large numbers of people, and the increase inthe use of these social networks to perform searches for jobs that havebeen posted on or linked to by the social networks.

Various preprocessing steps commonly performed on job search queries insocial networking services rely heavily on low-level natural languageprocessing such as tokenization, normalization, and the like, which arerelatively mature for popular languages such as English, French,Spanish, and so forth. However, for retrieval and ranking in otherlanguages, as well as cross-language retrieval (i.e., retrieval acrossmultiple languages with one search), erroneous low-level naturallanguage processing operations prohibit query expansions and rewriting,retrieval, and ranking processes fall back to basic keyword-basedsimilarity measures. What is needed is a way to obviate the need foradvanced low-level natural language processing operations in order toimprove cross-language retrieval.

Additionally, the tokenization and tagging performed by a query taggerand rewriter can be error prone, which propagates the errors to jobsretrieval and ranking phases. For example, consider the queries“software engineering manager” and “manager software engineering.” Theformer query is tagged as a title on the whole query, while the latterhas “manager” tagged as a title and “software engineering” tagged as askill. The impact of difference in tagging is a difference in queryconstruction, with the former query retrieving with an emphasis on titleonly and the latter query including jobs that match both the skill andthe title. Moreover, this difference in tagging propagates to a rankingphase, where tags for titles and skills contribute differently to theranking algorithm. What is needed is a way to reduce or eliminate thisretrieval degradation due to query preprocessing errors.

Furthermore, current job search mechanisms expand “important” tokens inqueries with their synonyms using a pre-defined list of similarkeywords. This manual step, however, is not scalable to differentdomains and locales. What is needed is a way to represent the query in away that obviates the need for this step.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the technology are illustrated, by way of exampleand not limitation, in the figures of the accompanying drawings.

FIG. 1 is a block diagram illustrating a client-server system, inaccordance with an example embodiment.

FIG. 2 is a block diagram showing the functional components of a socialnetworking service, including a data processing module referred toherein as a search engine, for use in generating and providing searchresults for a search query, consistent with some embodiments of thepresent disclosure.

FIG. 3 is a block diagram illustrating the application server module ofFIG. 2 in more detail, in accordance with an example embodiment.

FIG. 4 is a block diagram illustrating a network structure for DeepSemantic Similarity Measures (DSSM), in accordance with an exampleembodiment.

FIG. 5 is a block diagram illustrating a convolution matching model forclick through data, in accordance with an example embodiment.

FIG. 6 is a block diagram illustrating a deep neural network used tolearn semantic clustering, in accordance with an example embodiment.

FIG. 7 is a block diagram illustrating a system including a machinelearning model used for clustering, in accordance with an exampleembodiment.

FIG. 8 is a flow diagram illustrating a method to retrieve job postingresults produced in response to a query in a social networking service,in accordance with an example embodiment.

FIG. 9 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 10 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

Overview

The present disclosure describes, among other things, methods, systems,and computer program products that individually provide variousfunctionality. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the various aspects of different embodimentsof the present disclosure. It will be evident, however, to one skilledin the art, that the present disclosure may be practiced without all ofthe specific details.

In an example embodiment, a deep machine learning architecture is usedto retrieve semantically similar job postings to a job search query.Specifically, training systems are provided that can learn embeddings ofjob search queries and job postings into a semantic space that can bedefined mathematically. Additionally, a retrieval system is providedthat can score contextually (in query terms) similar documents (e.g.,job postings) efficiently. In some example embodiments, the embeddingsare stored in a forward index and a score is computed by iterating overall of the jobs as the retrieval phase. In other example embodiments,pre-defined clustering of documents is used with storing of the clusteridentifications in inverted indices. The result is that low recallqueries can be handled, where the number of documents scored in aretrieval phase is limited because of either limited (e.g., onlykeyword-based) query understanding, or because of a lack of a semanticmodel of document retrieval.

FIG. 1 is a block diagram illustrating a client-server system 100, inaccordance with an example embodiment. A networked system 102 providesserver-side functionality via a network 104 (e.g., the Internet or awide area network (WAN)) to one or more clients. FIG. I illustrates, forexample, a web client 106 (e.g., a browser) and a programmatic client108 executing on respective client machines 110 and 112.

An application program interface (API) server 114 and a web server 116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 118. The application server(s) 118host one or more applications 120. The application server(s) 118 are, inturn, shown to be coupled to one or more database servers 124 thatfacilitate access to one or more databases 126. While the application(s)120 are shown in FIG. 1 to form part of the networked system 102, itwill be appreciated that, in alternative embodiments, the application(s)120 may form part of a service that is separate and distinct from thenetworked system 102.

Further, while the client-server system 100 shown in FIG. 1 employs aclient-server architecture, the present disclosure is, of course, notlimited to such an architecture, and could equally well find applicationin a distributed, or peer-to-peer, architecture system, for example. Thevarious applications 120 could also be implemented as standalonesoftware programs, which do not necessarily have networkingcapabilities.

The web client 106 accesses the various applications 120 via the webinterface supported by the web server 116. Similarly, the programmaticclient 108 accesses the various services and functions provided by theapplication(s) 120 via the programmatic interface provided by the APIserver 114.

FIG. 1 also illustrates a third party application 128, executing on athird party server 130, as having programmatic access to the networkedsystem 102 via the programmatic interface provided by the API server114. For example, the third party application 128 may, utilizinginformation retrieved from the networked system 102, support one or morefeatures or functions on a website hosted by a third party. The thirdparty website may, for example, provide one or more functions that aresupported by the relevant applications 120 of the networked system 102.

In some embodiments, any website referred to herein may comprise onlinecontent that may be rendered on a variety of devices including, but notlimited to, a desktop personal computer (PC), a laptop, and a mobiledevice (e.g., a tablet computer, smartphone, etc.). In this respect, anyof these devices may be employed by a user to use the features of thepresent disclosure. In some embodiments, a user can use a mobile app ona mobile device (any of the machines 110, 112 and the third party server130 may be a mobile device) to access and browse online content, such asany of the online content disclosed herein. A mobile server (e.g., APIserver 114) may communicate with the mobile app and the applicationserver(s) 118 in order to make the features of the present disclosureavailable on the mobile device.

In some embodiments, the networked system 102 may comprise functionalcomponents of a social networking service. FIG. 2 is a block diagramshowing the functional components of a social networking service,including a data processing module referred to herein as a search engine216, for use in generating and providing search results for a searchquery, consistent with some embodiments of the present disclosure. Insome embodiments, the search engine 216 may reside on the applicationserver(s) 118 in FIG. 1. However, it is contemplated that otherconfigurations are also within the scope of the present disclosure.

As shown in FIG. 2, a front end may comprise a user interface module(e.g., a web server 116) 212, which receives requests from variousclient computing devices, and communicates appropriate responses to therequesting client devices. For example, the user interface module(s) 212may receive requests in the form of Hypertext Transfer Protocol (HTTP)requests or other web-based API requests. In addition, a memberinteraction detection module 213 may be provided to detect variousinteractions that members have with different applications 120,services, and content presented. As shown in FIG. 2, upon detecting aparticular interaction, the member interaction detection module 213 logsthe interaction, including the type of interaction and any metadatarelating to the interaction, in a member activity and behavior database222.

An application logic layer may include one or more various applicationserver modules 214, which, in conjunction with the user interfacemodule(s) 212, generate various user interfaces (e.g., web pages) withdata retrieved from various data sources in a data layer. In someembodiments, individual application server modules 214 are used toimplement the functionality associated with various applications 120and/or services provided by the social networking service.

As shown in FIG. 2, the data layer may include several databases, suchas a profile database 218 for storing profile data, including bothmember profile data and profile data for various organizations (e.g.,companies, schools, etc.). Consistent with some embodiments, when aperson initially registers to become a member of the social networkingservice, the person will be prompted to provide some personalinformation, such as his or her name, age (e.g., birthdate), gender,interests, contact information, home town, address, spouse's and/orfamily, members' names, educational background (e.g., schools, majors,matriculation and/or graduation dates, etc.), employment history,skills, professional organizations, and so on. This information isstored, for example, in the profile database 218. Similarly, when arepresentative of an organization initially registers the organizationwith the social networking service, the representative may be promptedto provide certain information about the organization. This informationmay be stored, for example, in the profile database 218, or anotherdatabase (not shown). In some embodiments, the profile data may beprocessed (e.g., in the background or offline) to generate variousderived profile data. For example, if a member has provided informationabout various job titles that the member has held with the sameorganization or different organizations, and for how long, thisinformation can be used to infer or derive a member profile attributeindicating the member's overall seniority level, or seniority levelwithin a particular organization. In some embodiments, importing orotherwise accessing data from one or more externally hosted data sourcesmay enrich profile data for both members and organizations. Forinstance, with organizations in particular, financial data may beimported from one or more external data sources and made part of anorganization's profile. This importation of organization data andenrichment of the data will be described in more detail later in thisdocument.

Once registered, a member may invite other members, or be invited byother members, to connect via the social networking service. A“connection” may constitute a bilateral agreement by the members, suchthat both members acknowledge the establishment of the connection.Similarly, in some embodiments, a member may elect to “follow” anothermember. In contrast to establishing a connection, the concept of“following” another member typically is a unilateral operation and, atleast in some embodiments, does not require acknowledgement or approvalby the member that is being followed. When one member follows another,the member who is following may receive status updates (e.g., in anactivity or content stream) or other messages published by the memberbeing followed, or relating to various activities undertaken by themember being followed. Similarly, when a member follows an organization,the member becomes eligible to receive messages or status updatespublished on behalf of the organization. For instance, messages orstatus updates published on behalf of an organization that a member isfollowing will appear in the member's personalized data feed, commonlyreferred to as an activity stream or content stream. In any case, thevarious associations and relationships that the members establish withother members, or with other entities and objects, are stored andmaintained within a social graph in a social graph database 220.

As members interact with the various applications 120, services, andcontent made available via the social networking service, the members'interactions and behavior (e.g., content viewed, links or buttonsselected, messages responded to, etc.) may be tracked, and informationconcerning the members' activities and behavior may be logged or stored,for example, as indicated in FIG. 2, by the member activity and behaviordatabase 222. This logged activity information may then be used by thesearch engine 216 to determine search results for a search query.

In some embodiments, the databases 218, 220, and 222 may be incorporatedinto the database(s) 126 in FIG. 1. However, other configurations arealso within the scope of the present disclosure.

Although not shown, in some embodiments, the social networking servicesystem 210 provides an API module via which applications 120 andservices can access various data and services provided or maintained bythe social networking service. For example, using an API, an applicationmay be able to request and/or receive one or more navigationrecommendations. Such applications 120 may be browser-based applications120, or may be operating system-specific. In particular, someapplications 120 may reside and execute (at least partially) on one ormore mobile devices (e.g., phone or tablet computing devices) with amobile operating system. Furthermore, while in many cases theapplications 120 or services that leverage the API may be applications120 and services that are developed and maintained by the entityoperating the social networking service. Nothing other than data privacyconcerns prevents the API from being provided to the public or tocertain third parties under special arrangements, thereby making thenavigation recommendations available to third party applications 128 andservices.

Although the search engine 216 is referred to herein as being used inthe context of a social networking service, it is contemplated that itmay also be employed in the context of any website or online services.Additionally, although features of the present disclosure are referredto herein as being used or presented in the context of a web page, it iscontemplated that any user interface view (e.g., a user interface on amobile device or on desktop software) is within the scope of the presentdisclosure.

In an example embodiment, when member profiles are indexed, forwardsearch indexes are created and stored. The search engine 216 facilitatesthe indexing and searching for content within the social networkingservice, such as the indexing and searching for data or informationcontained in the data layer, such as profile data (stored, e.g., in theprofile database 218), social graph data (stored, e.g., in the socialgraph database 220), and member activity and behavior data (stored,e.g., in the member activity and behavior database 222), as well as jobpostings. The search engine 216 may collect, parse, and/or store data inan index or other similar structure to facilitate the identification andretrieval of information in response to received queries forinformation. This may include, but is not limited to, forward searchindexes, inverted indexes, N-gram indexes, and so on.

FIG. 3 is a block diagram illustrating application server module 214 ofFIG. 2 in more detail, in accordance with an example embodiment. Whilein many embodiments the application server module 214 will contain manysubcomponents used to perform various different actions within thesocial networking system, in FIG. 3 only those components that arerelevant to the present disclosure are depicted. The application servermodule 214 includes a job posting query processor 300. The job postingquery processor 300 comprises a query ingestion component 302, whichreceives a user input “query” related to a job posting search via a userinterface (not pictured). Notably, this user input may take many forms.In some example embodiments, the user may explicitly describe a jobposting search query, such as by entering one or more keywords or termsinto one or more fields of a user interface screen. In other exampleembodiments, the job posting query may be inferred based on one or moreuser actions, such as selection of one or more filters, other jobposting searches by the user, searches for other members or entities,and so forth.

This “query” may be sent to a job posting database query formulationcomponent 304, which formulates an actual job posting database query,which will be sent via a job posting database interface 306 to jobposting database 308. Job posting results responsive to this job postingdatabase query may then be sent to the first pass job posting resultranking engine 310, again via the job posting database interface 306.The first pass job posting result ranking engine 310 then performs afirst pass at ranking the job posting results. A second pass job postingresult ranking engine 312 performs a second pass at ranking the jobposting results and then sends the ranked job posting results back tothe user interface for display to the user.

Traditionally, the job posting database query formulation component 304,first pass job posting result ranking engine 310, and second pass jobposting result ranking engine 312 all utilized keyword-based algorithmsthat did not factor in semantic similarity. In an example embodiment,one or more of these components are modified to factor in semanticsimilarity.

In an example embodiment, a Siamese network-based deep machine learningmodel is utilized. Here, a machine learning algorithm trains a pair ofmachine learning models, one for queries (utilized, for example, in thejob posting database query formulation component 304) and one for searchresults (utilized, for example, in the first pass job posting resultranking engine 310 and second pass job posting result ranking engine312). Parameters are shared between the models. In an exampleembodiment, this Siamese network-based machine learning algorithmutilizes DSSM. It acts to train a deep network model using clickthroughdata (e.g., the number of times members selected the job postingresults, or alternatively the number of times members applied for jobscorresponding to the job posting results), with cosine similarity on toplayer embeddings. FIG. 4 is a block diagram illustrating a networkstructure 400 for DSSM, in accordance with an example embodiment. Here,an embedding layer 401 acts to embed queries 402 and documents 404 (jobposting results) into corresponding high dimensionality vectors 406,408, respectively, containing one or more features. The highdimensionality vectors 406, 408 are then fed as input to a query-basedDSSM model 410 and document-based DSSM model 412, respectively, whicheach act to output concept vectors 414, 416, respectively, in alow-dimensional semantic feature space. A similarity layer 418 may thendetermine similarity of the concept vectors 414, 416 using, for example,cosine similarity.

In a convolutional variant of the above technique, a convolution istrained on the sequence of words in both the query and the document.

While the Siamese network-based deep machine learning model works wellfor single types of inputs (features such as company, locations, and thelike, it may fail to capture interactions between query and documentinput features. In an example embodiment, an interaction network-baseddeep machine learning model is utilized. The interaction network-baseddeep machine learning model captures feature interactions whileconstructing the deep networks. This can be extended with residualtraining that can train deeper layers with a residual-based objectivefunction. The interaction among features can be captured at differentlayers.

FIG. 5 is a block diagram illustrating a convolution matching model 500for click through data, in accordance with an example embodiment. Themodel 500 includes a matrix 502 formed from the combinations of queriesand documents. This matrix is fed through convolution and pooling layers504 to a multilayer perceptron (MLP) 506. The MLP 506 is a feedforwardartificial neural network model that maps sets of input data onto a setof appropriate outputs. The MLP 506 comprises multiple layers of nodesin a directed graph, with each layer fully connected to the next one.Except for the input nodes, each node is a neuron (processing element)with a nonlinear activation function. A supervised machine learningtechnique such as backpropagation may be used to train the MLP 506.

In an example embodiment, the retrieval phase (e.g., performed by thejob posting database query formulation component 304 of FIG. 3) makesuse of the DSSM to retrieve semantically similar job postings to a jobsearch query. Two approaches may be used. In a first approach, queryexpansion may be performed by adding additional terms to the searchquery, with those additional terms being those having semanticallysimilar keywords/phrases as terms in the job search query. In a secondapproach, job postings may be clustered and retrieval may be performedusing a query-based similar document process. The second approach hasthe advantage of improving performance in cases where there is nocontrol over the number of documents returned by a similar query(whereas clustering ensures a minimum number of documents).

Utilizing document clustering using embeddings as feature vectors hasthe limitation that a naively designed retrieval system would result inglobal similarity for documents, and query context would be ignored.Therefore, in an example embodiment, contextual retrieval is performedbased upon underlying clustered documents. In this embodiment, a mappingof query context to document context is constructed and the documentsare clustered simultaneously. At query time, a context for the jobsearch query may be calculated and its corresponding clusteringidentified. The corresponding clustering may then be used to retrievedocuments from an inverted index, using the document-cluster mapping.

In an example embodiment, DSSM is used for training a machine learningmodel to handle documents and queries. This training may utilize clickthrough data from job search queries. During the clustering phase,embeddings are taken from the last layer of the DSSM and used toperformed Gaussian Mixture modeling using a predefined number ofclusters. At query time, the query is passed through a query layer toconstruct the context of the query and identify its clusteringassignment.

FIG. 6 is a block diagram illustrating a deep neural network 600 used tolearn semantic clustering, in accordance with an example embodiment. Ahashing layer 602 hashes the incoming text of a query or job title.Specifically, the incoming text may get converted into multiple n-lettertokens. For example, if n is 3, then the query abc gets converted into#ab, abc, bc#, where # is the boundary token. Hashing provides thetechnical benefits of dimensionality reduction and out of vocabularyword representations.

An embedding layer 604 takes the previously hashed tokens and representsthem in a vector space. In an example embodiment, this vector space has300 dimensions. Non-linear activation layers 606A-606C comprise thelearning layers of the deep neural network. They may take, for example,tan h as the activation unit. Finally, a cosine similarity layer 608takes the representations from the query and job title and computescosine similarity among these representations. The cosine similarityscore may then be used as the feature in ranking documents.

In an example embodiment, the non-linear activation layers 606A-606Cwork as follows. If x is denoted as the input term vector, y as theoutput vector, l_(i), i=1, . . . , N−1, as the intermediate hiddenlayers, W_(i) as the i-th weight matrix, and b_(i) as the i-th biasterm, then:l₁=W₁xl _(i) =f(W _(i) l _(i−1) +b _(i)), i=2, . . . , N−1y=f(W _(N) l _(N−1) +b _(N))where tan h is used as the activation function at the output layer andthe hidden layers l_(i), i=2, . . . , N−1:

${f(x)} = \frac{1 - e^{{- 2}x}}{1 + e^{{- 2}x}}$

The semantic similarity score between a query Q and a document D is thenmeasured as

${R\left( {Q,D} \right)} = {{{cosine}\left( {y_{Q},y_{D}} \right)} = \frac{y_{Q}^{T}y_{D}}{{y_{Q}}{y_{D}}}}$where y_(Q) and y_(D) are the concept vectors of the query and document,respectively.

FIG. 7 is a block diagram illustrating a system 700 including a machinelearning model used for clustering, in accordance with an exampleembodiment. Here, an embedding layer 702 acts to embed queries 703 anddocuments 704 (job posting results) into corresponding highdimensionality vectors 706, 708, respectively, containing one or morefeatures. These embeddings may factor in the meaning of the input textterms. Specifically, the embeddings are such that the machine learningmodel is able to learn to distinguish, for example, a title (e.g.,software engineer) from a skill (e.g., Hadoop) and embed each term assuch. High dimensionality vectors 706, 708 are then fed as input to aquery-based DSSM model 710 and document-based DSSM model 712,respectively, which each act to output concept vectors 714, 716,respectively, in a low-dimensional semantic feature space. A similaritylayer 718 may determine similarity of the concept vectors 714, 716using, for example, cosine similarity. This similarity may be used inranking. A clustering algorithm may then be applied to the conceptvectors 714, 716 to derive their placement in clusters 720, 722. Here,both the concept vector 714 from the query context and the conceptvector 716 from the document context are placed in the same cluster 722.

An inverted index 724 may then be formed indicating, for each clusteridentification 726, which documents 728 are in the correspondingcluster. At query time, the input query may pass through the embeddinglayer 702 and query-based DSSM model 710 to derive a concept vector 714for the input query, which may then be mapped into the correspondingcluster 720, 722. Once a cluster identification for this cluster isdetermined, the inverted index 724 may be used to determine thedocuments within the cluster that correspond to the input query, thusallowing a retrieval phase to, for example, retrieve all of thedocuments in that cluster, regardless of whether or not exact keywordmatches for the input query are in those documents.

In some example embodiments, the documents from the matching cluster areinterleaved with documents retrieved using a token-based approach (e.g.,keyword matching) to produce a melded set of documents for retrieval.

Additionally, further clickthrough data (e.g., which documents membersclick on or whose underlying jobs they apply to) can be used to furthertrain/refine the models 710, 712. In this way, a feedback loop occurswhere the cluster that a selected document appears in is identified andused to train the models 710, 712 for future clustering.

In an example embodiment, the clustering algorithm may be a k-meansclustering algorithm or k-nearest neighbor clustering algorithm.

FIG. 8 is a flow diagram illustrating a method 800 to retrieve jobposting results produced in response to a query in a social networkingservice, in accordance with an example embodiment. This method 800 maybe divided into a training phase 802 and a retrieval phase 804. In thetraining phase 802, at operation 806, training data pertaining to samplejob posting search queries and sample job postings is obtained. Thetraining data may also comprise indications as to which of the samplejob posting search results were selected by members performingcorresponding job posting search queries. A loop is then begun for eachof the sample job posting search queries. At operation 808, the trainingdata corresponding to the job posting search query is fed into a firstmachine learning algorithm to train a query-based deep semanticsimilarity neural network to output a query context vector. At operation810, the query context vector is mapped to a cluster of query contextvectors and document context vectors using a clustering algorithm. Atoperation 812 it is determined if there are any additional sample jobposting search queries. If so, then the method 800 loops to operation808 for the next sample job posting search query. If not, then themethod 800 moves operation 814 where a loop is begun for each of thesample job postings. At operation 814, the training data correspondingto the sample job posting is fed into second first machine learningalgorithm to train a document-based deep semantic similarity neuralnetwork to output a document context vector. At operation 816, thedocument context vector is mapped to a cluster of query context vectorsand document context vectors using the clustering algorithm. Atoperation 818, it is determined if there are any additional sample jobpostings. If so, then the method 800 loops to operation 814 for the nextsample job posting. If not, then the method 800 moves to the retrievalphase 804.

Here, at operation 820, a job search query is received from a member ofthe social networking service. At operation 822, the job search query ispassed into the query-based deep semantic similarity neural network tooutput a first query context vector. At operation 824, the first querycontext vector is mapped to a cluster using the clustering algorithm.The identification of the cluster to which the first query contextvector is mapped is then used to retrieve one or more job postingscorresponding to the cluster identification at operation 826.

Modules, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium) orhardware modules. A “hardware module” is a tangible unit capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various example embodiments, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware modules ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware module may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware modulemay include software executed by a general-purpose processor or otherprogrammable processor. Once configured by such software, hardwaremodules become specific machines (or specific components of a machine)uniquely tailored to perform the configured functions and are no longergeneral-purpose processors. It will be appreciated that the decision toimplement a hardware module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware module at one instance oftime and to constitute a different hardware module at a differentinstance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented modules. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented modules may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented modules may be distributed across a number ofgeographic locations.

Machine and Software Architecture

The modules, methods, applications, and so forth described inconjunction with FIGS. 1-8 are implemented in some embodiments in thecontext of a machine and an associated software architecture. Thesections below describe representative software architecture(s) andmachine (e.g., hardware) architecture(s) that are suitable for use withthe disclosed embodiments.

Software architectures are used in conjunction with hardwarearchitectures to create devices and machines tailored to particularpurposes. For example, a particular hardware architecture coupled with aparticular software architecture will create a mobile device, such as amobile phone, tablet device, or so forth. A slightly different hardwareand software architecture may yield a smart device for use in the“Internet of Things,” while yet another combination produces a servercomputer for use within a cloud computing architecture. Not allcombinations of such software and hardware architectures are presentedhere, as those of skill in the art can readily understand how toimplement the inventive subject matter in different contexts from thedisclosure contained herein.

Software Architecture

FIG. 9 is a block diagram 900 illustrating a representative softwarearchitecture 902, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 9 is merely a non-limiting exampleof a software architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 902 may be executing onhardware such as a machine 1000 of FIG. 10 that includes, among otherthings, processors 1010, memory/storage 1030, and I/O components 1050. Arepresentative hardware layer 1000 is illustrated and can represent, forexample, the machine 1000 of FIG, 10. The representative hardware layer1000 comprises one or more processing units 1010. The executableinstructions 908 represent the executable instructions of the softwarearchitecture 902, including implementation of the methods, modules, andso forth of FIGS. 1-8. The hardware layer 1000 also includes memoryand/or storage modules 1030, which also have the executable instructions908.

In the example architecture of FIG. 9, the software architecture 902 maybe conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 902 mayinclude layers such as an operating system 904, libraries 906,frameworks/middleware 908, and applications 910. Operationally, theapplications 910 and/or other components within the layers may invokeAPI calls 912 through the software stack and receive responses, returnedvalues, and so forth, illustrated as messages 914, in response to theAPI calls 912. The layers illustrated are representative in nature andnot all software architectures have all layers. For example, some mobileor special-purpose operating systems may not provide a layer offrameworks/middleware 908, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 904 may manage hardware resources and providecommon services. The operating system 904 may include, for example, akernel 920, services 922, and drivers 924. The kernel 920 may act as anabstraction layer between the hardware and the other software layers.For example, the kernel 920 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 922 may provideother common services for the other software layers. The drivers 924 maybe responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 924 may include display drivers,camera drivers, Bluetooth® drivers, flash memory drivers, serialcommunication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi®drivers, audio drivers, power management drivers, and so forth dependingon the hardware configuration.

The libraries 906 may provide a common infrastructure that may beutilized by the applications 910 and/or other components and/or layers.The libraries 906 typically provide functionality that allows othersoftware modules to perform tasks in an easier fashion than byinterfacing directly with the underlying operating system 904functionality (e.g., kernel 920, services 922, and/or drivers 924). Thelibraries 906 may include system libraries 930 (e.g., C standardlibrary) that may provide functions such as memory allocation functions,string manipulation functions, mathematical functions, and the like. Inaddition, the libraries 906 may include API libraries 932 such as medialibraries (e.g., libraries to support presentation and manipulation ofvarious media formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, PNG),graphics libraries (e.g., an OpenGL framework that may be used to render2D and 3D graphic content on a display), database libraries (e.g.,SQLite that may provide various relational database functions), weblibraries (e.g., WebKit that may provide web browsing functionality),and the like. The libraries 906 may also include a wide variety of otherlibraries 934 to provide many other APIs to the applications 910 andother software components/modules.

The frameworks 908 (also sometimes referred to as middleware) mayprovide a higher-level common infrastructure that may be utilized by theapplications 910 and/or other software components/modules. For example,the frameworks 908 may provide various graphic user interface (GUI)functions, high-level resource management, high-level location services,and so forth. The frameworks 908 may provide a broad spectrum of otherAPIs that may be utilized by the applications 910 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system 904 or platform.

The applications 910 include third-party applications 966. Examples ofrepresentative built-in applications 966 may include, but are notlimited to, a contacts application, a browser application, a book readerapplication, a location application, a media application, a messagingapplication, and/or a game application. The third-party applications 966may include any of the built-in applications 940 as well as a broadassortment of other applications. In a specific example, the third-partyapplication 966 (e.g., an application developed using the Android™ oriOS™ software development kit (SDK) by an entity other than the vendorof the particular platform) may be mobile software running on a mobileoperating system such as iOS™, Android™, Windows® Phone, or other mobileoperating systems. In this example, the third-party application 966 mayinvoke the API calls 921 provided by the mobile operating system such asthe operating system 904 to facilitate functionality described herein.

The applications 910 may utilize built-in operating system 904 functions(e.g., kernel 920, services 922, and/or drivers 924), libraries 906(e.g., system libraries 930, API libraries 932, and other libraries934), and frameworks/middleware 908 to create user interfaces tointeract with users of the system. Alternatively, or additionally, insome systems, interactions with a user may occur through a presentationlayer, such as the presentation layer 944. In these systems, theapplication/module “logic” can be separated from the aspects of theapplication/module that interact with a user.

Example Machine Architecture and Machine-Readable Medium

FIG. 10 is a block diagram illustrating components of a machine 1000,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 10 shows a diagrammatic representation of the machine1000 in the example form of a computer system, within which instructions1016 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1000 to perform any oneor more of the methodologies discussed herein may be executed. Theinstructions 1016 transform the general, non-programmed machine into aparticular machine programmed to carry out the described and illustratedfunctions in the manner described. In alternative embodiments, themachine 1000 operates as a standalone device or may be coupled (e.g.,networked) to other machines. In a networked deployment, the machine1000 may operate in the capacity of a server machine or a client machinein a server-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 1000 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smart phone, amobile device, a wearable device (e.g., a smart watch), a smart homedevice (e.g., a smart appliance), other smart devices, a web appliance,a network router, a network switch, a network bridge, or any machinecapable of executing the instructions 1016, sequentially or otherwise,that specify actions to be taken by the machine 1000. Further, whileonly a single machine 1000 is illustrated, the term “machine” shall alsobe taken to include a collection of machines 1000 that individually orjointly execute the instructions 1016 to perform any one or more of themethodologies discussed herein.

The machine 1000 may include processors 1010, memory/storage 1030, andI/O components 1050, which may be configured to communicate with eachother such as via a bus 1002. In an example embodiment, the processors1010 (e.g., a central processing unit (CPU), a reduced instruction setcomputing (RISC) processor, a complex instruction set computing (CISC)processor, a graphics processing unit (GPU), a digital signal processor(DSP), an ASIC, a radio-frequency integrated circuit (RFIC), anotherprocessor, or any suitable combination thereof) may include, forexample, a processor 1012 and a processor 1014 that may execute theinstructions 1016. The term “processor” is intended to includemulti-core processors that may comprise two or more independentprocessors (sometimes referred to as “cores”) that may execute theinstructions 1016 contemporaneously. Although FIG. 10 shows multipleprocessors 1010, the machine 1000 may include a single processor 1012with a single core, a single processor 1012 with multiple cores (e.g., amulti-core processor 1012), multiple processors 1010 with a single core,multiple processors 1010 with multiples cores, or any combinationthereof.

The memory/storage 1030 may include a memory 1032, such as a mainmemory, or other memory storage, such as static memory 1034, and astorage unit 1036, both accessible to the processors 1010 such as viathe bus 1002. The storage unit 1036 and memory 1032 store theinstructions 1016 embodying any one or more of the methodologies orfunctions described herein. The instructions 1016 may also reside,completely or partially, within the memory 1032, within the storage unit1036, within at least one of the processors 1010 (e.g., within theprocessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 1000. Accordingly, the memory 1032, thestorage unit 1036, and the memory of the processors 1010 are examples ofmachine-readable media 1038.

As used herein, “machine-readable medium” means a device able to storeinstructions 1016 and data temporarily or permanently and may include,but is not limited to, random-access memory (RAM), read-only memory(ROM), buffer memory, flash memory, optical media, magnetic media, cachememory, other types of storage (e.g., erasable programmable read-onlymemory (EEPROM)), and/or any suitable combination thereof. The term“machine-readable medium” should be taken to include a single medium ormultiple media (e.g., a centralized or distributed database, orassociated caches and servers) able to store the instructions 1016. Theterm “machine-readable medium” shall also be taken to include anymedium, or combination of multiple media, that is capable of storinginstructions (e.g., instructions 1016) for execution by a machine (e.g.,machine 1000), such that the instructions 1016, when executed by one ormore processors of the machine 1000 (e.g., processors 1010), cause themachine 1000 to perform any one or more of the methodologies describedherein. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as “cloud-based” storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” excludes signals per se.

The I/O components 1050 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1050 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the 110 components1050 may include many other components that are not shown in FIG. 10.The I/O components 1050 are grouped according to functionality merelyfor simplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1050 mayinclude output components 1052 and input components 1054. The outputcomponents 1052 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1054 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1050 may includebiometric components 1056, motion components 1058, environmentalcomponents 1060, or position components 1062, among a wide array ofother components. For example, the biometric components 1056 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1058 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1060 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detect concentrations of hazardous gases for safetyor to measure pollutants in the atmosphere), or other components thatmay provide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 1062 mayinclude location sensor components (e.g., a Global Position System (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1050 may include communication components 1064operable to couple the machine 1000 to a network 1080 or devices 1070via a coupling 1082 and a coupling 1072, respectively. For example, thecommunication components 1064 may include a network interface componentor other suitable device to interface with the network 1080. In furtherexamples, the communication components 1064 may include wiredcommunication components, wireless communication components, cellularcommunication components, near field communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1070 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1064 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1064 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF47, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1064, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

Transmission Medium

In various example embodiments, one or more portions of the network 1080may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, the network 1080 or a portion of the network 1080may include a wireless or cellular network and the coupling 1082 may bea Code Division Multiple Access (CDMA) connection, a Global System forMobile communications (GSM) connection, or another type of cellular orwireless coupling. In this example, the coupling 1082 may implement anyof a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1× RTT), Evolution-Data Optimized(ENDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long rangeprotocols, or other data transfer technology.

The instructions 1016 may be transmitted or received over the network1080 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1064) and utilizing any one of a number of well-known transfer protocols(e.g., Hypertext Transfer Protocol (HTTP)). Similarly, the instructions1016 may be transmitted or received using a transmission medium via thecoupling 1072 (e.g., a peer-to-peer coupling) to the devices 1070. Theterm “transmission medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 1016 for execution by the machine 1000, and includesdigital or analog communications signals or other intangible media tofacilitate communication of such software.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the inventive subject matter may be referred to herein, individuallyor collectively, by the term “invention” merely for convenience andwithout intending to voluntarily limit the scope of this application toany single disclosure or inventive concept if more than one is, in fact,disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A system comprising: a non-transitorycomputer-readable medium having instructions stored thereon, which, whenexecuted by a processor, cause the system to: in a training phase:obtain training data pertaining to sample job posting search queries,the training data comprising sample job posting search results andindications as to which of the sample job posting search results wereselected by members performing corresponding job posting search queries;for each of the sample job posting search queries, feed thecorresponding training data into a first machine learning algorithm totrain a query-based deep semantic similarity neural network to output aquery context vector for an input job posting search query and into asecond machine learning algorithm to train a document-based deepsemantic similarity neural network to output a document context vectorfor an input job posting; map each query context vector and eachdocument context vector into a cluster using a clustering algorithm; ina retrieval phase: obtain a first job posting search query; pass thefirst job posting search query into the query-based deep semanticsimilarity neural network to output a first query context vector;identify a cluster in which to map the first document query vector usingthe clustering algorithm; and retrieve documents contained in a clustercorresponding to the identified cluster.
 2. The system of claim 1,wherein the instructions further cause the system to: add an entry foreach cluster identification to an inverted index, each entry furtherindicating a document mapped into a corresponding cluster; and theretrieving documents further comprising locating a first set of one ormore entries, in the inverted index, corresponding to the identificationof the cluster in which to map the first query context vector andretrieving any document indicated by the first set of one or moreentries.
 3. The system of claim 1, wherein the clustering algorithm is ak-means clustering algorithm.
 4. The system of claim 1, wherein theclustering algorithm is a k-nearest neighbor clustering algorithm. 5.The system of claim 1, wherein the retrieved documents are interleavedwith documents retrieved via a keyword-based retrieval process.
 6. Thesystem of claim 1, wherein the query-based deep semantic similarityneural network includes a hashing layer, an embedding layer, and aplurality of non-linear activation layers.
 7. The system of claim 6,wherein the plurality of non-linear activation layers have an activationunit of tan h.
 8. A computerized method, comprising in a training phase:obtaining training data pertaining to sample job posting search queries,the training data comprising sample job posting search results andindications as to which of the sample job posting search results wereselected by members performing corresponding job posting search queries;for each of the sample job posting search queries, feeding thecorresponding training data into a first machine learning algorithm totrain a query-based deep semantic similarity neural network to output aquery context vector for an input job posting search query and into asecond machine learning algorithm to train a document-based deepsemantic similarity neural network to output a document context vectorfor an input job posting; mapping each query context vector and eachdocument context vector into a cluster using a clustering algorithm; ina retrieval phase: obtaining a first job posting search query; passingthe first job posting search query into the query-based deep semanticsimilarity neural network to output a first query context vector;identify a cluster in which to map the first document query vector usingthe clustering algorithm; and retrieving documents contained in theidentified cluster.
 9. The method of claim 8, further comprising: addingan entry for each cluster identification to an inverted index, eachentry further indicating a document mapped into a corresponding cluster;and the retrieving documents further comprising locating a first set ofone or more entries, in the inverted index, corresponding to theidentification of the cluster in which to map the first query contextvector and retrieving any document indicated by the first set of one ormore entries.
 10. The method of claim 8, wherein the clusteringalgorithm is a k-means clustering algorithm.
 11. The method of claim 8,wherein the clustering algorithm is a k-nearest neighbor clusteringalgorithm.
 12. The method of claim 8, wherein the retrieved documentsare interleaved with documents retrieved via a keyword-based retrievalprocess.
 13. The method of claim 8, wherein the query-based deepsemantic similarity neural network includes a hashing layer, anembedding layer, and a plurality of non-linear activation layers. 14.The method of claim 13, wherein the plurality of non-linear activationlayers have an activation unit of tan h.
 15. A non-transitorymachine-readable storage medium comprising instructions, which whenimplemented by one or more machines, cause the one or more machines toperform operations comprising: in a training phase: obtaining trainingdata pertaining to sample job posting search queries, the training datacomprising sample job posting search results and indications as to whichof the sample job posting search results were selected by membersperforming corresponding job posting search queries; for each of thesample job posting search queries, feeding the corresponding trainingdata into a first machine learning algorithm to train a query-based deepsemantic similarity neural network to output a query context vector foran input job posting search query and into a second machine learningalgorithm to train a document-based deep semantic similarity neuralnetwork to output a document context vector for an input job posting;mapping each query context vector and each document context vector intoa cluster using a clustering algorithm; in a retrieval phase: obtaininga first job posting search query; passing the first job posting searchquery into the query-based deep semantic similarity neural network tooutput a first query context vector; identify a cluster in which to mapthe first document query vector using the clustering algorithm; andretrieving documents contained in the identified cluster.
 16. Thenon-transitory machine-readable storage medium of claim 15, furthercomprising: adding an entry for each cluster identification to aninverted index, each entry further indicating a document mapped into acorresponding cluster; and the retrieving documents further comprisinglocating a first set of one or more entries, in the inverted index,corresponding to the identification of the cluster in which to map thefirst query context vector and retrieving any document indicated by thefirst set of one or more entries.
 17. The non-transitorymachine-readable storage medium of claim 15, wherein the clusteringalgorithm is a k-means clustering algorithm.
 18. The non-transitorymachine-readable storage medium of claim 15, wherein the clusteringalgorithm is a k-nearest neighbor clustering algorithm.
 19. Thenon-transitory machine-readable storage medium of claim 15, wherein theretrieved documents are interleaved with documents retrieved via akeyword-based retrieval process.
 20. The non-transitory machine-readablestorage medium of claim 15, wherein the query-based deep semanticsimilarity neural network includes a hashing layer, an embedding layer,and a plurality of non-linear activation layers.